1. Field of the Invention
The present invention relates generally to a Multiple Input Multiple Output (MIMO) system, and in particular, to an apparatus and method for reducing a recalculation of a soft decision value in a MIMO system using an iterative detection and decoding scheme.
2. Description of the Related Art
With the rapid growth of mobile communication markets, various multimedia services for the wireless environment are in demand. In order to provide multimedia services in wireless communication systems, the development of technology to support a large capacity of transmit (TX) data and the high speed of data transfer are in progress. Therefore, the most urgent problem is to find methods that can efficiently use the limited frequencies in wireless communication systems. To solve the problem, wireless communication systems require new transfer techniques using multiple antennas. One of the new transfer techniques is a MIMO system using multiple antennas.
The MIMO system uses multiple antennas at a transmitter and a receiver. Compared with a system using a single antenna, the MIMO system can increase channel capacity in proportion to the number of antennas without additional frequency allocation or additional transmission power allocation.
MIMO technologies are classified into a spatial diversity scheme, a spatial multiplexing scheme, and a combination scheme of the spatial diversity and the spatial multiplexing. The spatial diversity scheme can obtain a diversity gain corresponding to a product of the number of TX antennas and the number of receive (RX) antennas, improving the transmission reliability. The spatial multiplexing scheme can increase the data rate by simultaneously transmitting a plurality of data streams.
When the spatial multiplexing scheme is used in the MIMO system, mutual interference occurs between the transmitted data streams. Therefore, the receiver detects signals using a maximum likelihood (ML) considering the influence of interference signals, or detects signals after interference cancellation. Examples of an interference cancellation scheme include a Zero Forcing (ZF) scheme and a Minimum Mean Square Error (MMSE) scheme.
However, the ML receiver has a drawback in that the complexity increases in proportion to the square of the number of TX antennas and the length of a codeword. Therefore, research on reception algorithms has been conducted which can reduce the calculation complexity of the receiver and obtain performance close to the ML receiver.
In addition, the MIMO system uses an iterative detection and decoding (IDD) scheme in which a turbo principle is applied to a MIMO receiver. In the MIMO IDD scheme, a single coder is concatenated with a channel coder and a MIMO coder. Thus, a MIMO detector of the MIMO receiver detects a signal received through an antenna and outputs the detection signal to a channel decoder. The channel decoder improves a bit performance by decoding the detection signal output from the MIMO detector and feeds back the decoded signal to the MIMO detector. The MIMO detector again generates a detection signal using the feedback signal. The MIMO receiver iteratively performs the above-described procedures. Examples of the MIMO IDD scheme include a list MIMO scheme and a turbo blast scheme. The two schemes have the same IDD scheme as the spatial multiplexing scheme of the transmitter, but are different in terms of the detection of the MIMO signal.
FIG. 1 is a block diagram of a conventional MIMO IDD receiver.
Referring to FIG. 1, when a signal is received, a MIMO detector 101 detects the received signal and generates a first soft decision data which is sent to a channel decoder 105 by deinterleaver 103. The soft decision information is a log likelihood ratio (LLR) value.
The channel decoder 105 decodes each bit using the first soft decision data as priori information and calculates a second soft decision value. That is, the channel decoder 105 corrects an error by decoding the first soft decision data. The second soft decision value calculated by the channel decoder 105 is fed back to the MIMO detector 101 by interleaver 107 and is used as the priori information for the IDD. By repeating the above-described procedures, the reliability of the received bits can be improved.
FIG. 2 is a flowchart illustrating an iteration process of the conventional MIMO IDD receiver.
Referring to FIG. 2, the MIMO IDD receiver detects a received ith MIMO node in step 201. That is, the MIMO IDD receiver generates a log likelihood ratio (LLR) value, which is a soft decision value of the ith MIMO node. “i” denotes an index of the MIMO node and its initial value is “1”.
In step 203, the MIMO IDD receiver determines if all the received MIMO nodes are detected. That is, the MIMO IDD receiver determines if the index i of the detected MIMO node is equal to a total number (NMIMO) of the MIMO nodes.
When all the received MIMO nodes are not detected (i≠NMIMO), the MIMO IDD receiver increases i by 1 in step 205 and returns to step 201 to generate an LLR value of an (i+1)th MIMO node.
When all the received MIMO nodes are detected (i=NMIMO), the MIMO IDD receiver proceeds to step 207 to deinterleave LLR values of the detected MIMO nodes. Thus, the MIMO nodes are rearranged.
In step 209, the MIMO IDD receiver decodes a jth decoding node among the deinterleaved LLR values. That is, the MIMO IDD receiver corrects an error by decoding the jth decoding node. “j” denotes an index of the decoding node and its initial value is “1”. The decoding node indicates the LLR value.
In step 211, the MIMO IDD receiver determines if all the decoding nodes are decoded. That is, the MIMO IDD receiver determines if the index j of the decoded decoding node is equal to a total number (Ndecoder) of the decoding nodes.
When all the decoding nodes are not decoded (j≠Ndecoder), the MIMO IDD receiver increases j by 1 in step 213 and returns to step 209 to generate an LLR value of an (j+1)th MIMO node.
When all the decoding nodes are decoded (j=Ndecoder), the MIMO IDD receiver proceeds to step 215 to determine if the number (N) of decoding iterations up to this point is equal to a preset total number (Niteration) of decoding iterations. That is, the MIMO IDD receiver determines if it has performed the total number of decoding iterations. “N” is an index representing the total number of the decoding iterations and its initial value is “1”.
When the MIMO IDD receiver has not performed the total number of the decoding iterations (N≠Niteration), it proceeds to step 217 to increase the number (N) of the decoding iterations by 1 and returns to step 209. At this point, the MIMO IDD receiver initializes the index (j) of the decoding node to “1”.
When the MIMO IDD receiver has performed the total number of the decoding iterations (N=Niteration), the MIMO IDD receiver determines if it has performed the IDD in step 219. That is, the MIMO IDD receiver determines if a current number (k) of IDDs is equal to a preset number (NIDD) of IDDs.
When the MIMO IDD receiver has not performed the total number of IDD iterations (k≠NIDD), it proceeds to step 221 to increase the current number of the IDD iterations by 1.
Then, the MIMO IDD receiver returns to step 201. At this point, the MIMO IDD receiver initializes the index (i) of the MIMO node, the index (j) of the decoding node, and the index (N) of the IDD iterations to “1”.
When the MIMO IDD receiver has performed the total number of the IDD iterations, it terminates the algorithm.
FIG. 3 is a conceptual diagram of the iteration process in the conventional MIMO IDD receiver.
Referring to FIG. 3, the MIMO detector 101 of the MIMO IDD receiver generates the soft decision values of the received MIMO nodes and transmits the generated soft decision values to the channel decoder 105 in step 301. In step 303, the channel decoder 105 decodes the soft decision values N times based on the received soft decision values and feeds back the decoded soft decision values to the MIMO detector 101.
Then, the MIMO IDD receiver repeats the above-described procedure as many times as the designated number of the IDD iterations.
As described above, the MIMO IDD scheme recalculates the soft decision value by feeding back the soft decision values calculated from the separated detector and decoder, thereby improving the performance of the MIMO system. However, because the IDD scheme calculates as many LLR values as the size of the coding blocks of the detector and decoder in each iteration, the complexity of the system increases as a total number of iterations increases.
In addition, when the iteration is performed, the detection of the MIMO nodes is performed and the decoding nodes are decoded using the detected soft decision values. The decoded soft decision values are used to detect the MIMO nodes. Then, these procedures are iteratively performed. Therefore, the conventional MIMO IDD receiver has a problem in that the transmission continuity of the soft decision values is degraded and the MIMO IDD performance convergence speed is lowered.